A numerical investigation of direct and indirect closed-loop architectures for estimating nonminimum-phase zeros

ABSTRACT

This paper presents a numerical investigation of three direct architectures and three indirect architectures for identifying a plant operating in closed loop. Motivated by adaptive control of systems with nonminimum-phase (NMP) zeros and taking advantage of the fact that zeros are not moved by feedback, the performance metric is the accuracy of the estimates of the NMP zeros of the plant. Assuming known plant order, single-input, single-output, infinite-impulse-response models are constructed in the presence of process and sensor noise. Least squares provides the baseline estimation technique, and prediction error methods are used to account for correlation between the model input and noise. The goal is to compare the accuracy of the NMP-zero estimates obtained from each method and for each architecture.

Keywords:

• System identification
• closed-loop identification
• auxiliary input
• prediction error method
• least squares
• consistency

Acknowledgments

The authors are grateful to the reviewers for their careful reading of the paper and numerous helpful suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported in part by National Science Foundation (NSF) [grant number CMMI 1536834].